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The version 4 version of this page is here.
See our Version 4 Migration Guide for information about how to upgrade.
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New to Plotly?¶
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You can set up Plotly to work in online or offline mode, or in jupyter notebooks.
We also have a quick-reference cheatsheet (new!) to help you get started!
Version Check¶
Plotly's python package is updated frequently. Run pip install plotly --upgrade to use the latest version.
In [1]:
import plotly
plotly.__version__
Out[1]:
Introduction¶
Aggregates are a type of transform that can be applied to values in a given expression. Available aggregations are:
| Function | Description |
|---|---|
count |
Returns the quantity of items for each group. |
sum |
Returns the summation of all numeric values. |
avg |
Returns the average of all numeric values. |
median |
Returns the median of all numeric values. |
mode |
Returns the mode of all numeric values. |
rms |
Returns the rms of all numeric values. |
stddev |
Returns the standard deviation of all numeric values. |
min |
Returns the minimum numeric value for each group. |
max |
Returns the maximum numeric value for each group. |
first |
Returns the first numeric value for each group. |
last |
Returns the last numeric value for each group. |
Basic Example¶
In [2]:
import plotly.offline as off
off.init_notebook_mode(connected=False)
subject = ['Moe','Larry','Curly','Moe','Larry','Curly','Moe','Larry','Curly','Moe','Larry','Curly']
score = [1,6,2,8,2,9,4,5,1,5,2,8]
data = [dict(
type = 'scatter',
x = subject,
y = score,
mode = 'markers',
transforms = [dict(
type = 'aggregate',
groups = subject,
aggregations = [dict(
target = 'y', func = 'sum', enabled = True),
]
)]
)]
off.iplot({'data': data}, validate=False)
Aggregate Functions¶
In [3]:
import plotly.offline as off
off.init_notebook_mode(connected=False)
subject = ['Moe','Larry','Curly','Moe','Larry','Curly','Moe','Larry','Curly','Moe','Larry','Curly']
score = [1,6,2,8,2,9,4,5,1,5,2,8]
aggs = ["count","sum","avg","median","mode","rms","stddev","min","max","first","last"]
agg = []
agg_func = []
for i in range(0, len(aggs)):
agg = dict(
args=['transforms[0].aggregations[0].func', aggs[i]],
label=aggs[i],
method='restyle'
)
agg_func.append(agg)
data = [dict(
type = 'scatter',
x = subject,
y = score,
mode = 'markers',
transforms = [dict(
type = 'aggregate',
groups = subject,
aggregations = [dict(
target = 'y', func = 'sum', enabled = True)
]
)]
)]
layout = dict(
title = '<b>Plotly Aggregations</b><br>use dropdown to change aggregation',
xaxis = dict(title = 'Subject'),
yaxis = dict(title = 'Score', range = [0,22]),
updatemenus = [dict(
x = 0.85,
y = 1.15,
xref = 'paper',
yref = 'paper',
yanchor = 'top',
active = 1,
showactive = False,
buttons = agg_func
)]
)
off.iplot({'data': data,'layout': layout}, validate=False)
Histogram Binning¶
In [4]:
import plotly.offline as off
import pandas as pd
off.init_notebook_mode(connected=False)
df = pd.read_csv("https://plotly.com/~public.health/17.csv")
data = [dict(
x = df['date'],
autobinx = False,
autobiny = True,
marker = dict(color = 'rgb(68, 68, 68)'),
name = 'date',
type = 'histogram',
xbins = dict(
end = '2016-12-31 12:00',
size = 'M1',
start = '1983-12-31 12:00'
)
)]
layout = dict(
paper_bgcolor = 'rgb(240, 240, 240)',
plot_bgcolor = 'rgb(240, 240, 240)',
title = '<b>Shooting Incidents</b>',
xaxis = dict(
title = '',
type = 'date'
),
yaxis = dict(
title = 'Shootings Incidents',
type = 'linear'
),
updatemenus = [dict(
x = 0.1,
y = 1.15,
xref = 'paper',
yref = 'paper',
yanchor = 'top',
active = 1,
showactive = True,
buttons = [
dict(
args = ['xbins.size', 'D1'],
label = 'Day',
method = 'restyle',
), dict(
args = ['xbins.size', 'M1'],
label = 'Month',
method = 'restyle',
), dict(
args = ['xbins.size', 'M3'],
label = 'Quater',
method = 'restyle',
), dict(
args = ['xbins.size', 'M6'],
label = 'Half Year',
method = 'restyle',
), dict(
args = ['xbins.size', 'M12'],
label = 'Year',
method = 'restyle',
)]
)]
)
off.iplot({'data': data,'layout': layout}, validate=False)
Mapping with Aggregates¶
In [5]:
import plotly.offline as off
import pandas as pd
off.init_notebook_mode(connected=False)
df = pd.read_csv("https://raw.githubusercontent.com/bcdunbar/datasets/master/worldhappiness.csv")
aggs = ["count","sum","avg","median","mode","rms","stddev","min","max","first","last"]
agg = []
agg_func = []
for i in range(0, len(aggs)):
agg = dict(
args=['transforms[0].aggregations[0].func', aggs[i]],
label=aggs[i],
method='restyle'
)
agg_func.append(agg)
data = [dict(
type = 'choropleth',
locationmode = 'country names',
locations = df['Country'],
z = df['HappinessScore'],
autocolorscale = False,
colorscale = 'Portland',
reversescale = True,
transforms = [dict(
type = 'aggregate',
groups = df['Country'],
aggregations = [dict(
target = 'z', func = 'sum', enabled = True)
]
)]
)]
layout = dict(
title = '<b>Plotly Aggregations</b><br>use dropdown to change aggregation',
xaxis = dict(title = 'Subject'),
yaxis = dict(title = 'Score', range = [0,22]),
height = 600,
width = 900,
updatemenus = [dict(
x = 0.85,
y = 1.15,
xref = 'paper',
yref = 'paper',
yanchor = 'top',
active = 1,
showactive = False,
buttons = agg_func
)]
)
off.iplot({'data': data,'layout': layout}, validate=False)
Reference¶
See https://plotly.com/python/reference/ for more information and chart attribute options!
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